
%run "/code/source/config/notebook_settings.py"


pd.set_option('display.max_colwidth', None)


from source.library.text_analysis import count_tokens, tf_idf, get_context_from_keyword, \
    count_keywords, count_keywords_by, impurity


with Timer("Loading Data"):
    path = 'artifacts/data/processed/reddit.pkl'
    df = pd.read_pickle(path)

2022-06-29 01:29:46 - INFO     | Timer Started: Loading Data
2022-06-29 01:29:47 - INFO     | Timer Finished: (0.68 seconds)


df.head(1)


hlp.pandas.numeric_summary(df)


hlp.pandas.non_numeric_summary(df)


df['post'].iloc[0][0:1000]

"Hello, <lb><lb>I have my J32A3 egine open, ready to put new rings and all. I would like to know if I can swap the J35Z2 crank and rods from an Accord Sedan v6 3.5. I would like to keep my pistons, just change the rods and crank to have a little more displacement. This is a doable option? there is something I need to know first? This is for my Acura TL 3G 2004 Manual Transmission. I really would like to know if it's possible and if possible what differences are between then J35Z2 and let's say the classic J35A3.<lb><lb>Thanks in advance."


'|'.join(df['partial_lemmas'].iloc[0])[0:1000]

'hello|j32a3|egine|open|ready|new|ring|like|know|swap|j35z2|crank|rod|accord|sedan|v6|_number_|like|piston|change|rod|crank|little|displacement|doable|option|need|know|acura|tl|3|g|_number_|manual|transmission|like|know|possible|possible|difference|j35z2|let|classic|j35a3|thank|advance'


'|'.join(df['bi_grams'].iloc[0])[0:1000]

'j32a3 egine|egine open|new ring|j35z2 crank|accord sedan|sedan v6|v6 _number_|doable option|acura tl|tl 3|3 g|g _number_|_number_ manual|manual transmission|classic j35a3'


'|'.join(df['noun_phrases'].iloc[0])[0:1000]

'egine open|new ring|doable option'


ax = df['impurity'].plot(kind='box', vert=False, figsize=(10, 1))
ax.set_title("Distribution of Post Impurity")
ax.set_xlabel("Impurity")
ax.set_yticklabels([])
ax;


df[['impurity', 'post', 'post_clean']].sort_values('impurity', ascending=False).head()


df['language'].value_counts(ascending=False)

English    4996
Name: language, dtype: int64


df['subreddit'].value_counts(ascending=False)

Lexus                 266
Hyundai               263
Trucks                262
Honda                 261
MPSelectMiniOwners    260
mercedes_benz         259
mazda3                257
Harley                255
volt                  252
Volkswagen            252
Audi                  252
teslamotors           250
Volvo                 249
Mustang               248
BMW                   239
saab                  239
4Runner               238
Porsche               236
subaru                233
Wrangler              229
Name: subreddit, dtype: int64


remove_tokens = {'_number_', 'car'}
count_tokens(df['partial_lemmas'], remove_tokens=remove_tokens).head(10)


ax = df['post_length'].plot(kind='box', vert=False, figsize=(10, 1))
ax.set_title("Distribution of Post Length")
ax.set_xlabel("# of Characters")
ax.set_yticklabels([])
ax;


ax = df['post_length'].plot(kind='hist', bins=60, figsize=(10, 2));
ax.set_title("Distribution of Post Length")
ax.set_xlabel("# of Characters")
ax;


import seaborn as sns
sns.displot(df['post_length'], bins=60, kde=True, height=3, aspect=3);


where = df['subreddit'].isin([
    'Lexus', 
    'mercedes_benz',
    'Audi',
    'Volvo',
    'BMW',
])
g = sns.catplot(data=df[where], x="subreddit", y="post_length", kind='box')
g.fig.set_size_inches(6, 3)
g.fig.set_dpi(100)
g = sns.catplot(data=df[where], x="subreddit", y="post_length", kind='violin')
g.fig.set_size_inches(6, 3)
g.fig.set_dpi(100)


counts_df = count_tokens(df['partial_lemmas'], remove_tokens=remove_tokens)


def plot_wordcloud(frequency_dict):
    wc = wordcloud.WordCloud(background_color='white',
        #colormap='RdYlGn',
        colormap='tab20b',
        width=round(hlp.plot.STANDARD_WIDTH*100),
        height=round(hlp.plot.STANDARD_HEIGHT*100),
        max_words = 200, max_font_size=150,
        random_state=42
    )
    wc.generate_from_frequencies(frequency_dict)

    fig, ax = plt.subplots(figsize=(hlp.plot.STANDARD_WIDTH, hlp.plot.STANDARD_HEIGHT))
    ax.imshow(wc, interpolation='bilinear')
    #plt.title("XXX")
    plt.axis('off')


plot_wordcloud(counts_df.to_dict()['frequency']);


tf_idf_lemmas = tf_idf(
    df=df,
    tokens_column='partial_lemmas',
    segment_columns = None,
    min_frequency_corpus=20,
    min_frequency_document=20,
    remove_tokens=remove_tokens,
)
tf_idf_lemmas.head()


remove_tokens_bi_grams = {'_number_ year', '_number_ _number_', 'hey guy'}
tf_idf_bi_grams = tf_idf(
    df=df,
    tokens_column='bi_grams',
    segment_columns = None,
    min_frequency_corpus=20,
    min_frequency_document=20,
    remove_tokens=remove_tokens_bi_grams,
)
tf_idf_bi_grams.head()


tf_idf_nouns = tf_idf(
    df=df,
    tokens_column='nouns',
    segment_columns = None,
    min_frequency_corpus=20,
    min_frequency_document=20,
    remove_tokens=remove_tokens,
)
tf_idf_nouns.head()


tf_idf_noun_phrases = tf_idf(
    df=df,
    tokens_column='noun_phrases',
    segment_columns = None,
    min_frequency_corpus=20,
    min_frequency_document=20,
    remove_tokens=remove_tokens_bi_grams,
)
tf_idf_noun_phrases.head()


ax = tf_idf_lemmas.head(30)[['tf-idf']].plot(kind='barh', width=0.99)
ax.set_title("TF-IDF of Uni-Grams")
ax.set_xlabel("TF-IDF")
ax.invert_yaxis();


ax = tf_idf_bi_grams.head(30)[['tf-idf']].plot(kind='barh', width=0.99)
ax.set_title("TF-IDF of Bi-Grams")
ax.set_xlabel("TF-IDF")
ax.invert_yaxis();


ax = tf_idf_nouns.head(30)[['tf-idf']].plot(kind='barh', width=0.99)
ax.set_title("TF-IDF of Bi-Grams")
ax.set_xlabel("TF-IDF")
ax.invert_yaxis();


ax = tf_idf_noun_phrases.head(30)[['tf-idf']].plot(kind='barh', width=0.99)
ax.set_title("TF-IDF of Bi-Grams")
ax.set_xlabel("TF-IDF")
ax.invert_yaxis();


plot_wordcloud(tf_idf_lemmas.to_dict()['tf-idf']);


plot_wordcloud(tf_idf_bi_grams.to_dict()['tf-idf']);


remove_tokens_subreddit = set(df.subreddit.str.lower().unique())
remove_tokens_subreddit

{'4runner',
 'audi',
 'bmw',
 'harley',
 'honda',
 'hyundai',
 'lexus',
 'mazda3',
 'mercedes_benz',
 'mpselectminiowners',
 'mustang',
 'porsche',
 'saab',
 'subaru',
 'teslamotors',
 'trucks',
 'volkswagen',
 'volt',
 'volvo',
 'wrangler'}


tf_idf_lemmas_per_sub = tf_idf(
    df=df,
    tokens_column='partial_lemmas',
    segment_columns = 'subreddit',
    min_frequency_corpus=10,
    min_frequency_document=10,
    remove_tokens=remove_tokens | remove_tokens_subreddit 
)
tf_idf_lemmas_per_sub.head(5)


tf_idf_bigrams_per_sub = tf_idf(
    df=df,
    tokens_column='bi_grams',
    segment_columns = 'subreddit',
    min_frequency_corpus=10,
    min_frequency_document=10,
    remove_tokens=remove_tokens_bi_grams
)
tf_idf_bigrams_per_sub.head(5)


tf_idf_nouns_per_sub = tf_idf(
    df=df,
    tokens_column='nouns',
    segment_columns = 'subreddit',
    min_frequency_corpus=10,
    min_frequency_document=10,
    remove_tokens=remove_tokens | remove_tokens_subreddit
)
tf_idf_nouns_per_sub.head(5)


tf_idf_nounphrases_per_sub = tf_idf(
    df=df,
    tokens_column='noun_phrases',
    segment_columns = 'subreddit',
    min_frequency_corpus=10,
    min_frequency_document=10,
    remove_tokens=remove_tokens_bi_grams
)
tf_idf_nounphrases_per_sub.head(5)


tokens_to_show = tf_idf_lemmas_per_sub.query("subreddit in ['Lexus', 'Volvo']").reset_index()
tokens_to_show.head()


px.bar(
    tokens_to_show.groupby(['subreddit']).head(20).sort_values('tf-idf', ascending=True),
    x='tf-idf',
    y='token',
    color='subreddit',
    barmode='group',
    title="Top 20 Lemmas for Volvo & Lexus"
)


tokens_to_show = tf_idf_bigrams_per_sub.query("subreddit in ['Lexus', 'Volvo']").reset_index()
tokens_to_show.head()


px.bar(
    tokens_to_show.groupby(['subreddit']).head(20).sort_values('tf-idf', ascending=True),
    x='tf-idf',
    y='token',
    color='subreddit',
    barmode='group',
    title="Top 20 Bi-Grams for Volvo & Lexus"
)


get_context_from_keyword(df.query("subreddit == 'Lexus'")['post'], keyword='think')

["I bought the latest version, I  |think|  it's 13.1 (haven't looked at it in",
 'd additional stops can be added. I  |think|  we have a good grasp of the Mid an',
 ' etc.) 3] Share any cool ideas you  |think|  might be interesting to attempt in',
 "ents and career moves lately and I  |think|  it's about time I finally upgrade.",
 'y forums such as Club Lexus, and I  |think|  it is an issue with the arms, beca',
 'n the sunshade went up properly, I  |think|  it was because my coaxing got it p',
 'han the other a little bit. Also I  |think|  may be catching in the slit that t',
 'er and I have looked around. I was  |think| ing of getting these:<lb><lb>https:',
 '17-2b5389f9c7dd<lb><lb>Do you guys  |think|  its worth it? or is there a better',
 "ok for?<lb><lb>So what do you guys  |think| ? I've found a similar 2007 Lexus E"]


get_context_from_keyword(df.query("subreddit == 'Volvo'")['post'], keyword='think')

['o, I ordered a replacement switch,  |think| ing that some leads got messed up w',
 'do going forward? Only thing I can  |think|  of is that the wiring harness has ',
 'ch on the car is slightly leaky. I  |think|  it is because of the "rubber seals',
 "is true when I see it in person, I  |think|  I'm OK buying it with the knowledg",
 'lso drove it and had no issues. He  |think| s maybe height related?  I loved th',
 "y xc90 is has a rough idle and I'm  |think| ing it's the MAF. I cleaned it and ",
 ' valve seal issue. What do you all  |think| ? Is the cost of repair worth this ',
 'ience are the any other issues you  |think|  I should address to avoid future p',
 ' already said, it runs great and I  |think|  it has a lot of miles left on it i',
 "Hi guys!<lb>I'm  |think| ing about having my front seats dee"]


tokens_to_show = tf_idf_lemmas_per_sub.query("subreddit == 'Lexus'").reset_index()
#tokens_to_show = tokens_to_show[~tokens_to_show.token.isin(stop_words)]
tokens_to_show = tokens_to_show[['token', 'tf-idf']].set_index('token')
tokens_to_show = tokens_to_show.to_dict()['tf-idf']
plot_wordcloud(tokens_to_show);


tokens_to_show = tf_idf_lemmas_per_sub.query("subreddit == 'Volvo'").reset_index()
#tokens_to_show = tokens_to_show[~tokens_to_show.token.isin(stop_words)]
tokens_to_show = tokens_to_show[['token', 'tf-idf']].set_index('token')
tokens_to_show = tokens_to_show.to_dict()['tf-idf']
plot_wordcloud(tokens_to_show);


contexts = get_context_from_keyword(
    documents=df['post'],
    window_width=50,
    keyword='replac',
    num_samples = 20,
    random_seed=42
)
for x in contexts:
    print(x)

rger.<lb>* My Elantra still runs great and I just  |replac| ed the tires.<lb>* The financials are all there, b
 for a while, but now the time has come for me to  |replac| e my coil springs. I had heard great things about 
I'm  |replac| ing the radio with a Pioneer AVH-X2800BS in my '04
<lb><lb>I sent it back to PA Performance and they  |replac| ed all the internals. While I was waiting on PA Pe
 Also, the battery in the car seems like it needs  |replac| ing as the interior lights flicker and the car has
ere can confirm that before I go digging deep for  |replac| ement panels. Thanks!
and my steering stabilizer is shot.  Do I need to  |replac| e it to keep my tires healthy or not?  I really do
nyone knows the type of them? Or where can i find  |replac| ements? I will add pictures, the small one is from
 is having some problems so that might need to be  |replac| ed. Could that explain the floatiness? <lb><lb>Als
<lb>The coolant reservoir tank seems to have been  |replac| ed, I heard it's a common problem on these.
Anyone else experience this? Dealer quote to  |replac| e it was insane for what I imagine is a relatively
I know the gasket needs to be  |replac| ed and the mechanic i take mine too agreed to let 
I also asked Mazda service dealers for a quote in  |replac| ing this part. The quotes varied wildly with some 
 it not for corrosion around the old part.<lb><lb> |Replac| ing the vehicle speed sensor, or driven-speed gear
lb>My boss has a 2010 Dodge Ram 1500 and wants to  |replac| e his old, corroded bars with some new ones. Thing
 the things that I am seeing is pretty much about  |replac| ing the entire intake manifold. Also seeing a lot 
My fault, I fucked up  |replac| ing the door lock actuator and now my rear passeng
 may bite the bullet and buy a used door panel to  |replac| e it with unless you guys have any suggestions. 
gs, they were original, appear to have aged well.  |Replac| ed the plugs, and switched the coil pack from 1-2 
 every now and then it simply won't start. I have  |replac| ed and checked the battery, but I'm thinking I may

	# of Non-Nulls	% Nulls	# of Zeros	% Zeros	Mean	St Dev.	Coef of Var	Skewness	Kurtosis	Min	10%	25%	50%	75%	90%	Max
impurity	5,000	0.0%	1,023	20.0%	0.0	0.0	1.0	1.9	7.8	0.0	0.0	0.0	0.0	0.0	0.0	0.2
post_length	5,000	0.0%	0	0.0%	679.1	452.5	0.7	2.5	9.2	256.0	307.0	381.0	538.0	812.2	1,217.1	4,174.0
num_tokens	5,000	0.0%	0	0.0%	54.2	36.3	0.7	2.6	10.0	15.0	25.0	31.0	43.0	65.0	96.0	380.0

	# of Non-Nulls	# of Nulls	% Nulls	Most Freq. Value	# of Unique	% Unique
id	5,000	0	0.0%	74qv99	5,000	100.0%
subreddit	5,000	0	0.0%	Lexus	20	0.4%
title	5,000	0	0.0%	Need some advice	4,995	99.9%
post	5,000	0	0.0%	Hello, <lb><lb>I have my J32A3[...]	5,000	100.0%
post_clean	5,000	0	0.0%	Hello, I have my J32A3 egine o[...]	5,000	100.0%
all_lemmas	5,000	0	0.0%	['hello', 'i', 'have', 'my', '[...]	5,000	100.0%
partial_lemmas	5,000	0	0.0%	['hello', 'j32a3', 'egine', 'o[...]	5,000	100.0%
bi_grams	5,000	0	0.0%	['j32a3 egine', 'egine open', [...]	5,000	100.0%
adjs_verbs	5,000	0	0.0%	['open', 'ready', 'new', 'like[...]	5,000	100.0%
nouns	5,000	0	0.0%	['j32a3', 'egine', 'ring', 'j3[...]	5,000	100.0%
noun_phrases	5,000	0	0.0%	[]	4,986	99.7%
entities	5,000	0	0.0%	[]	4,242	84.8%
language	4,996	4	0.1%	English	1	0.0%

	impurity	post	post_clean
12357	0.18	I'm looking to lease an a4 premium plus automatic with the nav package.<lb><lb>Vehicle Price:<tab><tab>$49,150.00<tab> <lb> <tab>AutoNation Savings:<tab>-<tab>$3,867.00<tab> <lb> <tab>AutoNation Price:<tab><tab>$45,283.00<tab> <lb> <tab> <tab> <lb> <tab>Sales Tax (estimate):<tab>+<tab>$2,734.98<tab> <lb> <tab>Title Fee:<tab>+<tab>$100.00<tab> <lb> <tab>Tire/Battery/MVWEA:<tab>+<tab>$4.00<tab> <lb> <tab>Tag/Registration Fees (estimate):<tab>+<tab>$207.00<tab> <lb> <tab>Electronic Filing:<tab>+<tab>$20.00<tab> <lb> <tab>Other:<tab>+<tab>$20.00<tab> <lb> <tab>Documentation Fee:<tab>+<tab>$300.00<tab> <lb> <tab>Balance Due (estimate):<tab><tab>$48,668.98<tab> <tab>No Trade-In<lb><lb>LEASE OPTIONS<lb>Cash Due<tab>36 months <tab>42 months <lb><lb>$2,000 <tab>$723<tab>$690<lb>$4,000 <tab>$663<tab>$639<lb>$6,000 <tab>$603<tab>$587<lb><lb><lb>This is my first lease, do these numbers look good? Should I push back or negotiate on anything?<lb><lb>Thanks!	I'm looking to lease an a4 premium plus automatic with the nav package. Vehicle Price: $ _NUMBER_ AutoNation Savings: $ _NUMBER_ AutoNation Price: $ _NUMBER_ Sales Tax (estimate): $ _NUMBER_ Title Fee: $ _NUMBER_ Tire/Battery/MVWEA: $ _NUMBER_ Tag/Registration Fees (estimate): $ _NUMBER_ Electronic Filing: $ _NUMBER_ Other: $ _NUMBER_ Documentation Fee: $ _NUMBER_ Balance Due (estimate): $ _NUMBER_ No Trade-In LEASE OPTIONS Cash Due _NUMBER_ months _NUMBER_ months $ _NUMBER_ $ _NUMBER_ $ _NUMBER_ $ _NUMBER_ $ _NUMBER_ $ _NUMBER_ $ _NUMBER_ $ _NUMBER_ $ _NUMBER_ This is my first lease, do these numbers look good? Should I push back or negotiate on anything? Thanks!
12754	0.17	Bulbs Needed:<lb><lb><lb>194 LED BULB x8<lb><lb>4- DOORS<lb><lb>2- MAP LIGHTS<lb><lb>2- VANITY<lb><lb><lb>3022 LED BULB x3<lb><lb>2- CARGO DOOR<lb><lb>1- DOME LIGHT<lb><lb><lb>BULBS USED:<lb><lb>[194 LED BULBS](https://goo.gl/Jfu2Dx)<lb><lb>[3022 LED BULBS](https://goo.gl/fPgk6n)<lb><lb>[Trim Tools](https://goo.gl/hjxw8Z)<lb><lb>Parts list courtesy of [The Blue TRD](https://www.youtube.com/watch?v=CBJxfWdbEfo&t=28s) from his You Tube Channel.<lb><lb>Just passing along the helpful info.	Bulbs Needed: _NUMBER_ LED BULB x8 _NUMBER_ - DOORS _NUMBER_ - MAP LIGHTS _NUMBER_ - VANITY _NUMBER_ LED BULB x3 _NUMBER_ - CARGO DOOR _NUMBER_ - DOME LIGHT BULBS USED: _NUMBER_ LED BULBS _NUMBER_ LED BULBS Trim Tools Parts list courtesy of The Blue TRD from his You Tube Channel. Just passing along the helpful info.
2730	0.15	Breakdown below:<lb><lb>Elantra GT<lb><lb>2.0L 4-cylinder<lb><lb>6-speed Manual Transmission<lb><lb>$19,350<lb><lb>Elantra GT<lb><lb>2.0L 4-cylinder<lb><lb>6-speed Automatic Transmission w/ SHIFTRONIC®<lb><lb>$20,350<lb><lb>Elantra GT Sport<lb><lb>1.6L Turbo GDI 4-cylinder<lb><lb>6-speed Manual Transmission<lb><lb>$23,250<lb><lb>Elantra GT Sport<lb><lb>1.6L Turbo GDI 4-cylinder<lb><lb>7-speed EcoShift® Dual Clutch Transmission w/ SHIFTRONIC®<lb><lb>$24,350	Breakdown below: Elantra GT _NUMBER_ .0L _NUMBER_ -cylinder _NUMBER_ -speed Manual Transmission $ _NUMBER_ Elantra GT _NUMBER_ .0L _NUMBER_ -cylinder _NUMBER_ -speed Automatic Transmission w/ SHIFTRONIC® $ _NUMBER_ Elantra GT Sport _NUMBER_ .6L Turbo GDI _NUMBER_ -cylinder _NUMBER_ -speed Manual Transmission $ _NUMBER_ Elantra GT Sport _NUMBER_ .6L Turbo GDI _NUMBER_ -cylinder _NUMBER_ -speed EcoShift® Dual Clutch Transmission w/ SHIFTRONIC® $ _NUMBER_
8700	0.13	E-price:<lb>$20,863.00<lb>Freight:<lb>$900.00<lb>Processing Fee:<lb>$299.00<lb>Total before tax and tag fees:<lb>$22,062.00<lb>7% State SALES TAX: $ 1,544.34<lb>2 YEAR TAG FEES: $187.00<lb>TITLE: $100.00<lb>REGISTRATION: $20.00<lb>LIEN: $20.00<lb>INSPECTION: $25.00 <lb>State TEMP TAG: $20.00<lb>State TIRE FEE: $4.00<lb>TOTAL OUT THE DOOR YOU REQUESTED: $ 23,982.34<lb>	E-price: $ _NUMBER_ Freight: $ _NUMBER_ Processing Fee: $ _NUMBER_ Total before tax and tag fees: $ _NUMBER_ _NUMBER_ % State SALES TAX: $ _NUMBER_ _NUMBER_ YEAR TAG FEES: $ _NUMBER_ TITLE: $ _NUMBER_ REGISTRATION: $ _NUMBER_ LIEN: $ _NUMBER_ INSPECTION: $ _NUMBER_ State TEMP TAG: $ _NUMBER_ State TIRE FEE: $ _NUMBER_ TOTAL OUT THE DOOR YOU REQUESTED: $ _NUMBER_
12522	0.12	The lease on my 2014 C250 is ending and I have the option to buy it for $20k.<lb>So I decided to see what else is out there for about the same amount of money and so far this are my options:<lb> <lb> <lb>Model: C250<lb>Year: 2014<lb>Miles: 7k<lb>Price: $20k<lb> <lb>Model: E350<lb>Year: 2010<lb>Miles: 59k<lb>Price: $19k<lb> <lb>Model: ML350<lb>Year: 2006<lb>Miles: 42k<lb>Price: $13k<lb> <lb>Thoughts?	The lease on my _NUMBER_ C250 is ending and I have the option to buy it for $20k. So I decided to see what else is out there for about the same amount of money and so far this are my options: Model: C250 Year: _NUMBER_ Miles: 7k Price: $20k Model: E350 Year: _NUMBER_ Miles: 59k Price: $19k Model: ML350 Year: _NUMBER_ Miles: 42k Price: $13k Thoughts?

	frequency	tf-idf
token
look	2777	3044.75
drive	1878	2872.63
like	2355	2830.75
new	1738	2577.63
mile	1402	2505.93

	frequency	tf-idf
token
$ _number_	1089	2366.46
_number_ mile	553	1417.20
look like	229	749.94
_number_ month	193	673.05
test drive	189	673.02

Citation¶

Setup¶

Exploratory Data Analysis¶

Dataset Summary¶

Numeric Summary¶

Non-Numeric¶

Examples¶

Explore Non-Text Columns¶

Impurity¶

Explore Text Column|¶

Top Words Used¶

Distribution of Text Length¶

Word Frequency¶

TF-IDF¶

By Subreddit¶

Lexus¶

Volvo¶

Keywords in Context¶

	frequency	tf-idf
token
mile	1401	2504.15
issue	1143	2242.28
year	1229	2227.66
time	1288	2214.70
engine	945	2102.89

	frequency	tf-idf
token
_number_ mile	428	1188.36
_number_ %	139	539.46
oil change	132	514.63
_number_ month	131	503.89
new car	130	502.27

		frequency	tf-idf
subreddit	token
4Runner	gen	74	283.40
	sr5	50	241.65
	lift	57	215.97
	rear	61	171.54
	look	149	163.37

	subreddit	token	frequency	tf-idf
0	Lexus	is350	37	198.29
1	Lexus	mile	102	182.31
2	Lexus	look	166	182.01
3	Lexus	gs	30	160.77
4	Lexus	drive	104	159.08

	subreddit	token	frequency	tf-idf
0	Lexus	$ _number_	54	117.35
1	Lexus	_number_ lexus	19	105.00
2	Lexus	f sport	16	94.55
3	Lexus	_number_ is250	13	82.09
4	Lexus	es _number_	13	80.85

		frequency	tf-idf
subreddit	token
4Runner	_number_ 4runner	11	68.41
	3rd gen	11	66.57
	sway bar	12	66.31
	_number_ mile	22	61.08
	check engine	10	44.28